Statistical methods and data-driven algorithms are gaining notice and being used as marketing tools. Here are the seven most common techniques and applications.
Intuition and creativity are marketing pillars, but in the last decade, data and predictive techniques have started to be regarded as a new philosophy, as opposed to simple tools. Hunches, surveys and focus groups are not getting enough clients in the marketing funnel to have satisfactory results.
Predictive analysis uses historical data and statistical techniques together with machine learning to compute the probabilities of future outcomes based on past experiences. For marketing purposes, this approach can be used to reverse-engineer customer experiences and identify winning strategies to replicate them for similar results. There are appropriate tools and algorithms for each stage of the retail journey, from lead acquisition to post-sale customer care.
Predictive analysis expert Thomas Miller lists the areas of marketing that could benefit most from using data-driven models. These include understanding the markets, creating 360-degree consumer models, using social networks, and positioning, promoting and developing products. Competition analysis and creating new market research models are also on the list. Let’s explore the seven most common techniques in predictive analysis with associated marketing applications.
1. Data visualization
The first and easiest step in using predictive analytics for marketing is getting accustomed to data by creating visual representations. This approach requires almost no technical knowledge; a simple spreadsheet program will be enough. People understand and can react faster to visual representations of information. In a marketing context, this approach is valuable for studying client demographics, deciding between multiple product types or looking at the most profitable regions for distribution. Any data initiative should use visualization as a primary and powerful tool, since it can uncover relations in the sets, groups and untapped potential.
Knowing the odds of a customer performing a particular action after they have seen an ad, found an offer or downloaded a free item is important when you’re trying to move along the sales funnel. Conditional probabilities are extremely useful in this case, where the condition is some characteristic of the lead, like age or income. Probabilities also help compute the lift of a certain subgroup of a given group. For example, if 1 in 1,000 individuals will buy a phone, but 2 in 1,000 teenagers will do the same, that means that age group has a lift of 2, which gives valuable insight about advertising targeting.
3. Regression and correlation
Marketers are interested in determining action triggers in clients. Understanding the if-then cycle in the consumer’s mind is the key to selling more. While correlation identifies possible links between variables that have an influence on an outcome, regression analysis can quantify the magnitude of this impact. As one of the fundamental ways of predictive analysis, regression helps the analyst build models that can give actionable insights on necessary steps, like renovating locations to increase attractiveness and boost sales.
4. Time series analysis
Marketing is a process, not a one-time action. Therefore, it helps to look at it from a timeline perspective to observe peaks and lows, seasonality, and other patterns. A time series can record the number of visitors to a website, sales or any other set of data taken at regular intervals like daily, weekly or monthly. By applying statistical analysis methods, a data scientist can uncover patterns and predict future behavior of the same series. For example, if the product is ice cream, you can expect seasonal changes, but just how large those will be can only be computed by a time series.
5. Clustering algorithms
Market segmentation is necessary for precise customer behavior targeting. Clustering algorithms group elements together by a common denominator, like behavior, products used and purchased, or preferred brands. If a client is classified in a certain cluster, that helps recommendation engines perform more accurately by offering relevant suggestions.
6. Data mining and machine learning
A step further from the simple statistical analysis is data mining, a technique of building algorithms that recognizes patterns in large amounts of raw data. Machine learning refers to the capacity of computers to be “trained” to identify such patterns and take decisions based on their findings. Marketing can benefit from such advancements by implementing dynamic A/B testing based on user preferences. Results could be displayed immediately and the process repeated until the customer is mesmerized by the degree of personalization they receive. The most important lesson here is that there is no “one size fits all.”
7. Text and sentiment analysis
The explosion of social media and review sites has triggered the need to combine existing tools into a new one designed for market predictions. Text and sentiment analysis are using semantic knowledge, data mining and deep learning to extract communication patterns from user postings and evaluate brand perception. After a web scraper tool retrieves the content, an algorithm divides the words into clusters based on the underlying sentiment and extracts the predominant reactions toward a product or the whole brand. You can find some interesting examples of this approach in recent studies from data science consulting firm InData Labs. The role is to assess market share, positioning the brand in relation to competitors and evaluating if there is any image crisis. This is more of a combined method, but with its vast implications in marketing, it deserves its place.
Like most data-driven initiatives, using predictive analytics in marketing is slowed down by data that has not been collected for the purpose it is used, is not clean or has missing values. Data quality is the most important issue, followed by overfitting of the model. This term is defined as finding some patterns in the data just for the sake of the method. Only use data models when these are relevant to the question you are trying to answer, not just to join the hype.
Marketing is successfully using tools from data science. The goals of the discipline have not changed; the central questions still revolve around closing sales, getting better conversion rates, operating cost-effectively and optimizing marketing campaigns. Predictive analytics are here just to connect the old descriptive approach to the new prescriptive one.